Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light OPEN ACCESSRemote Sens. 2015, 7 9046 detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R 2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

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Abbreviations IS = imaging spectroscopy; LiDAR = light detection and ranging; N:P ratio = nitrogen to phosphorus ratio; N = nitrogen; P = phosphorus Nomenclature Little (1979) Abstract Questions: Can the ratio of nitrogen to phosphorus (N:P ratio) be predicted at canopy level using imaging spectroscopy (IS) and light detection and ranging (LiDAR) remote sensing data? How do temporal variation and difference in spatial resolution of these data sources affect prediction accuracy of the canopy N:P ratio?Location: Boreal mixedwood forest, northern Ontario, Canada.Methods: Canopy N:P ratio was estimated using spectral indices calculated from IS data at two spatial resolutions, airborne and space-borne, across two summers. The relationship between the canopy N:P ratio and forest structure was investigated through analysis of LiDAR data. The impact of temporal variation on canopy N:P ratio and the different spatial resolution of IS data on prediction accuracy for canopy N:P was addressed. Maps of canopy N:P ratio generated from airborne and space-borne IS data were generated.Results: Airborne and space-borne IS data explained 70% and 69% of the variation in canopy N:P, ratio, with predictions errors of 5.0% and 7.2%, respectively, in two consecutive years. Predictions differed significantly with changes in spatial resolution. Predictive models obtained from LiDAR data explained 54% and 67% of the variation in canopy N:P ratio, with prediction errors of 6.1% and 7.5%, respectively, for the 2 yrs. Conclusions:The results show that canopy N:P ratio can be predicted with remote sensing data based on the relationship between canopy N:P ratio and crown closure at this site. The spatial variation due to the mixed deciduous and coniferous forest type is the underlying mechanism that generates the observed spatial pattern in canopy N:P ratio in this ecosystem, and the canopy N:P ratio map displays this variation.

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This study investigates the relationship between fatality and magnitude, energy released, time of earthquake occurrence and building damage, and between building damage and magnitude and energy of earthquakes that occurred in Turkey between 1900 and 2012. Patterns of earthquake occurrence and damage are examined across space and time. Ninety-one per cent and eighty-three per cent of the variation in fatality and building damage are accounted for by the energy released from an earthquake, respectively. Building damage explains 85% of the variation in fatality. Seventy-six per cent of the total death toll is a result of the earthquakes that happened between midnight and 5:22 am. There were two significant clusters of earthquakes in Western Turkey between 1955 and 1965 and between 1969 and 1971. Provinces of Erzincan and Kocaeli emerge as hot spots of fatality and building damage. The location of major earthquakes over 112 years tends to parallel the tectonically active areas of Turkey such as the North and East Anatolian Faults and Western Turkey towards the Aegean Sea. Central, central south, extreme north-western, north-eastern and south-eastern Turkey appears to be void of major earthquakes.

Objective:The objective of this study was to determine the extent and degree of severity of a burned area resulting from a forest fire using Sentinel 2 remote sensing data in Çanakkale, Turkey within the Mediterranean Basin, an area of the world where forest fire occurrence and severity are increasing. Materials and Methods: Pre and postfire Sentinel images were obtained. The Normalized Burn Ratio (NBR) index was calculated for each scene. Then the difference NBR (dNBR) was calculated by subtracting the postfire NBR from the prefire NBR. dNBR ranges were classified into fire severity categories. A map with 20 m spatial resolution displaying the burned area and fire severity was generated from the classified dNBR image. Finally, a forest stand map of the burn area was laid over the fire severity map to examine the relationship between fire severity and stand and cover types. Results: Approximately 1400 ha of area was predicted to have been burned. Twenty nine, 21, 42, and 8% of the burned area was identified as low, moderate low, moderate high, and severely burned using the dNBR index, respectively. Conclusions: The overlay of the stand map on the burn severity map revealed that the forested areas were more severely burned compared to the agricultural sections. dNBR is an effective index to delineate fire area extent and identify fire severity. Sentinel 2 data provide a fast and accurate means to monitor forest fire extent and severity due to its improved spatial and temporal resolution.